Properties. Our dataset follows a long tail distribution with the number of collected examples ranging from 38 (Place Metal Bar - only occurring once per assembly) to 610 (Pick up Washer and/or Screw) with average clip lengths ranging from as short as 0.96 seconds (Put Down Spanner) to 12.89 seconds (Mark Screw(s) with Pen). Total clip lengths of classes range from as low as 46 seconds (Put Down Spanner) to over 30 minutes (Tightening Screw with Screwdriver). While relatively modest in size our dataset is emblematic of many dataset collected within real world environments where the collection and labelling of large datasets is challenging due to quick turnaround times and domain novelty. Compared to other action segmentation datasets ours is much more fine-grained than large datasets such as Breakfast [1]} (77 hours - 10 actions) and larger in size than comparative fine-grained datasets such as GTEA [2]} (28 minutes - 20 actions) and 50 salads [3]} (4 hours - 17 actions). Our dataset is noticeably fine-grained as the background remains constant throughout all classes, with only subtle changes in spatial and temporal information between many classes. In addition, due to the noun/verb pairing process there are overlaps between some classes where the same noun (e.g tool or component) appears within multiple verb pairings (e.g screw or place) and vice versa. As such the ability to model both spatial and temporal information will be key to accurate classification.
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